A Simple Multivariate Filter for the Measurement of Potential Output
Bibliographic record
Abstract
This paper examines techniques that have been used to estimate potential output and finds them wanting. We suggest a simple multivariate-filtering technique that is a generalization of the Hodrick-Prescott univariate filter. In univariate filters, only information about a variable itself is used in eliminating noise in order to obtain an estimate of the underlying trend. We suggest a generalization, wherein other information is used to sharpen the identification of potential output. For example, we note that, if movements in potential output have a different effect on inflation than do cyclical movements in output, then information on inflation may be useful in identifying potential output. The prospects for improving measures of potential output by using this and other information in the multivariate filter are demonstrated through Monte Carlo experiments. Evidence is also presented contrasting the results of using the multivariate filter on the historical Canadian data with the results from the Hodrick-Prescott filter and other, more traditional methods of estimating potential output. We argue that the multivariate filter has advantages over quasi-structural models of potential output because it can exploit general information from economic theory about what information might be useful, without imposing restrictions from imperfect representations of the true structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".